I worked as a research intern at SONY US Research Center for kernel metric learning and ranking, and a research intern at Microsoft Research Asia for graphical model based feature learning. I got my BS in Electronic Engineering from Tsinghua University in 2004.

My general passion is to design effecient and elegant solution to solve challenging problems in real world by a combination of machine learning, optimization and statistics.

1. On the theoretical side, my current research focuses on analytic analysis for Robust Manifold Learning and Tensor Voting.
These are unsupervised learning methods for modeling high dimensional data and multivariate time series.

DMW is an unsupervised similarity learning and alignment algorithm for structured multivariate time series. Under the spatio-temporal manifold model, DMW can align two series with different length, dimensionality and sampling frequency.

LLD is a non-linear and non-parametric denoising algorithm for high-dimensional data with the intrinsic manifold strucutre. LLD denoises the manifold by jointly optimizing the local to global alignment error and graph Laplacian (Laplacian-Bertrami) energy.

Though there are many image decomposition methods, it is hard to get both of
the basis and the features to be independent without the normal
distribution assumption. Recent research shows that sparseness and
other constraints will lead to part-based representations results,
which is similar to the receptive fields in V1 cortex in human Brain.
Sparse Coding, Sparse Bayesian Learning and Compressive Sensing have
been proposed for pattern learning, feature extraction, denoising and
compression during the past 10 years.

In
vision, self-taught learning means studying the knowledge from
free-cost images in our natural environment; it is an active area in
machine learning in recent years. The significance of self-taught
learning is to revisit the fact that sometimes not only the labeled
target data but also the relevant unlabeled data are hard to get, while
at the same time the basic patterns can be embedded in the general data
although it is unlabeled and with quite different distribution.

Propose
a model-based feature extraction approach, which uses micro-structure
modeling to design adaptive micro-patterns. We first model the
micro-structure of the image by Pair-wise Markov Random Field. Then we
give the generalized definition of micro-pattern based on the model.
After that, we define the fitness function and compute the fitness
index to encode the image’s local fitness to micro-patterns.

During the spare time, I love watching movies and listening pop music.
Especially, I was a director in TDO (TDO means trade-off, it is
extremely important for team work^_^) studio, Tsinghua University. We
made two student movies and one music video. One of the movie is "how do I love you", which got the best idea and best actor prizes in the first Tsinghua Digital Movie Festival, 2004.

Here is the link of this movie in youku. Actually, I just found it out by accident and I really do not know who put this online, but it is fine, enjoy it:-).